Hi All, The discussion on the gufunc signature enhancements seems to have stalled a bit, but while it was going I've tried to update the NEP correspondingly. The NEP is now merged, so can viewed more easily, at http://www.numpy.org/neps/nep-0020-gufunc-signature-enhancement.html
My own quite possibly biased summary of the discussion so far is that: 1) Frozen dimensions are generally seen as a good idea; other implementations may be possible, but are not as clear. 2) Flexible dimensions have little use beyond matmul; the main discussion is whether there is a better way. In my opinion, the main benefit of the current proposal is that it allows operator overrides to all work the same way (via __array_ufunc__), independent of any assumptions about the object that does the override (such as that it has a shape). 3) Broadcastable dimensions had less support, but mostly for lack of examples; there now is one beyond all_equal, for which a gufunc is more clearly the proper route: a weighted average (which has obvious extensions). A general benefit of course is that there is actual code for all three; it would certainly be nice if we could fully support `matmul` and `@` in 1.16. So, the question would seem whether the NEP should be accepted or rejected (with part acceptance of course being possible, though I note that flexible and broadcastable share a lot of implementation, so in my opinion it is somewhat pointless to do just one of them). All the best, Marten
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